
arXiv: 1609.02094
For any integers $d, n \geq 2$ and $1/({\min\{n,d\}})^{0.4999} < \varepsilon<1$, we show the existence of a set of $n$ vectors $X\subset \mathbb{R}^d$ such that any embedding $f:X\rightarrow \mathbb{R}^m$ satisfying $$ \forall x,y\in X,\ (1-\varepsilon)\|x-y\|_2^2\le \|f(x)-f(y)\|_2^2 \le (1+\varepsilon)\|x-y\|_2^2 $$ must have $$ m = Ω(\varepsilon^{-2} \lg n). $$ This lower bound matches the upper bound given by the Johnson-Lindenstrauss lemma [JL84]. Furthermore, our lower bound holds for nearly the full range of $\varepsilon$ of interest, since there is always an isometric embedding into dimension $\min\{d, n\}$ (either the identity map, or projection onto $\mathop{span}(X)$). Previously such a lower bound was only known to hold against linear maps $f$, and not for such a wide range of parameters $\varepsilon, n, d$ [LN16]. The best previously known lower bound for general $f$ was $m = Ω(\varepsilon^{-2}\lg n/\lg(1/\varepsilon))$ [Wel74, Lev83, Alo03], which is suboptimal for any $\varepsilon = o(1)$.
v2: simplified proof, also added reference to Lev83
Computational Geometry (cs.CG), FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), random projections, Functional Analysis (math.FA), Mathematics - Functional Analysis, Computer Science - Data Structures and Algorithms, Johnson-Lindenstrauss, FOS: Mathematics, Computer Science - Computational Geometry, Data Structures and Algorithms (cs.DS), dimensionality reduction
Computational Geometry (cs.CG), FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), random projections, Functional Analysis (math.FA), Mathematics - Functional Analysis, Computer Science - Data Structures and Algorithms, Johnson-Lindenstrauss, FOS: Mathematics, Computer Science - Computational Geometry, Data Structures and Algorithms (cs.DS), dimensionality reduction
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